Reservoir Lithology Determination by Hidden Markov Random Fields Based on a Gaussian Mixture Model

In this paper, geological prior information is incorporated in the classification of reservoir lithologies after the adoption of Markov random fields (MRFs). The prediction of hidden lithologies is based on measured observations, such as seismic inversion results, which are associated with the latent categorical variables, based on the...

Determination of Reservoir Lithology from Seismic Data by a 2D Hidden Markov Random Field Model

In this study, geological prior information is incorporated in the classification of reservoir lithologies using the Markov Random Field (MRF) technique. The prediction of hidden lithologies in seismic data is based on measured<br/>observations such as seismic inversion results, which are associated with the latent categorical variables derived...

Reservoir lithology determination from seismic inversion results using markov processes

For reservoir characterization, the subsurface heterogeneity needs to be qualified in which the distribution of lithologies is an essential part since it determines the location and migration paths of hydrocarbons. Preliminary analysis of well-log data could help to identify various lithologies in a one-dimensional direction (depth), while the...

An Outcrop-based Detailed Geological Model to Test Automated Interpretation of Seismic Inversion Results

Previously, Tetyukhina et al. (2014) developed a geological and petrophysical model based on the Book Cliffs outcrops that contained eight lithotypes. For reservoir modelling purposes, this model is judged to be too coarse because in the same lithotype it contains reservoir and non-reservoir lithologies. Hence, a new and more detailed geological...